{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on this:  \n",
    "\n",
    "https://stackabuse.com/python-for-nlp-movie-sentiment-analysis-using-deep-learning-in-keras/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow==1.15.2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (1.15.2)\n",
      "Requirement already satisfied: wrapt>=1.11.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==1.15.2) (1.12.1)\n",
      "Requirement already satisfied: tensorflow-estimator==1.15.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==1.15.2) (1.15.1)\n",
      "Requirement already satisfied: tensorboard<1.16.0,>=1.15.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==1.15.2) (1.15.0)\n",
      "Requirement already satisfied: numpy<2.0,>=1.16.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==1.15.2) (1.18.2)\n",
      "Requirement already satisfied: keras-preprocessing>=1.0.5 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==1.15.2) (1.1.0)\n",
      "Requirement already satisfied: google-pasta>=0.1.6 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==1.15.2) (0.2.0)\n",
      "Requirement already satisfied: six>=1.10.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==1.15.2) (1.11.0)\n",
      "Requirement already satisfied: astor>=0.6.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==1.15.2) (0.8.1)\n",
      "Requirement already satisfied: absl-py>=0.7.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==1.15.2) (0.9.0)\n",
      "Requirement already satisfied: grpcio>=1.8.6 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==1.15.2) (1.27.2)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==1.15.2) (3.2.0)\n",
      "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==1.15.2) (0.31.1)\n",
      "Requirement already satisfied: keras-applications>=1.0.8 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==1.15.2) (1.0.8)\n",
      "Requirement already satisfied: protobuf>=3.6.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==1.15.2) (3.6.1)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==1.15.2) (1.1.0)\n",
      "Requirement already satisfied: gast==0.2.2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==1.15.2) (0.2.2)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.2) (3.2.1)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.2) (0.14.1)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.2) (46.1.3)\n",
      "Requirement already satisfied: h5py in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from keras-applications>=1.0.8->tensorflow==1.15.2) (2.8.0)\n",
      "Collecting keras\n",
      "  Using cached Keras-2.3.1-py2.py3-none-any.whl (377 kB)\n",
      "Requirement already satisfied: six>=1.9.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from keras) (1.11.0)\n",
      "Requirement already satisfied: numpy>=1.9.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from keras) (1.18.2)\n",
      "Requirement already satisfied: keras-preprocessing>=1.0.5 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from keras) (1.1.0)\n",
      "Requirement already satisfied: pyyaml in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from keras) (5.3.1)\n",
      "Requirement already satisfied: h5py in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from keras) (2.8.0)\n",
      "Requirement already satisfied: scipy>=0.14 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from keras) (1.1.0)\n",
      "Requirement already satisfied: keras-applications>=1.0.6 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from keras) (1.0.8)\n",
      "Installing collected packages: keras\n",
      "Successfully installed keras-2.3.1\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow==1.15.2\n",
    "!pip install keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "import sagemaker\n",
    "import pandas as pd\n",
    "\n",
    "sess   = sagemaker.Session()\n",
    "bucket = sess.default_bucket()\n",
    "role = sagemaker.get_execution_role()\n",
    "region = boto3.Session().region_name\n",
    "\n",
    "sm = boto3.Session().client(service_name='sagemaker', region_name=region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101836, 15)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/amazon_reviews_us_Digital_Software_v1_00.tsv.gz', delimiter='\\t')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101831, 16)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned_and_filtered = df.dropna()\n",
    "df_cleaned_and_filtered = df_cleaned_and_filtered.reset_index()\n",
    "df_cleaned_and_filtered.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_body</th>\n",
       "      <th>is_positive_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>So far so good</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Needs a little more work.....</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Please cancel.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Works as Expected!</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I've had Webroot for a few years. It expired a...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         review_body  is_positive_sentiment\n",
       "0                                     So far so good                      1\n",
       "1                      Needs a little more work.....                      0\n",
       "2                                     Please cancel.                      0\n",
       "3                                 Works as Expected!                      1\n",
       "4  I've had Webroot for a few years. It expired a...                      1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned_and_filtered['is_positive_sentiment'] = (df_cleaned_and_filtered['star_rating'] >= 4).astype(int)\n",
    "df_cleaned_and_filtered = df_cleaned_and_filtered[['review_body', 'is_positive_sentiment']]\n",
    "df_cleaned_and_filtered.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb2a7b98e10>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.countplot(x='is_positive_sentiment', data=df_cleaned_and_filtered)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38876\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import resample\n",
    "\n",
    "is_negative_sentiment_df = df_cleaned_and_filtered.query('is_positive_sentiment == 0')\n",
    "\n",
    "is_positive_sentiment_df = df_cleaned_and_filtered.query('is_positive_sentiment == 1')\n",
    "print(len(is_negative_sentiment_df))\n",
    "\n",
    "is_positive_downsampled_df = resample(is_positive_sentiment_df,\n",
    "                                replace = False, # sample without replacement\n",
    "                                n_samples = len(is_negative_sentiment_df), # match minority n\n",
    "                                random_state = 27) # reproducible results\n",
    "\n",
    "df_cleaned_and_filtered_and_balanced = pd.concat([is_negative_sentiment_df, is_positive_downsampled_df])\n",
    "#df_cleaned_and_filtered_and_balanced.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb2a7bcf5f8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='is_positive_sentiment', data=df_cleaned_and_filtered_and_balanced)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import nltk\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Version:  1.15.2\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(\"Version: \", tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# TODO:  Convert this to TF 2.0 tf.keras\n",
    "import os\n",
    "os.environ['KERAS_BACKEND'] = 'tensorflow'\n",
    "import keras\n",
    "\n",
    "from numpy import array\n",
    "from keras.preprocessing.text import one_hot\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Activation, Dropout, Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import GlobalMaxPooling1D\n",
    "from keras.layers.embeddings import Embedding\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.preprocessing.text import Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text(sen):\n",
    "    # Removing html tags\n",
    "    sentence = remove_tags(sen)\n",
    "\n",
    "    # Remove punctuations and numbers\n",
    "    sentence = re.sub('[^a-zA-Z]', ' ', sentence)\n",
    "\n",
    "    # Single character removal\n",
    "    sentence = re.sub(r\"\\s+[a-zA-Z]\\s+\", ' ', sentence)\n",
    "\n",
    "    # Removing multiple spaces\n",
    "    sentence = re.sub(r'\\s+', ' ', sentence)\n",
    "\n",
    "    return sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "TAG_RE = re.compile(r'<[^>]+>')\n",
    "\n",
    "def remove_tags(text):\n",
    "    return TAG_RE.sub('', text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = []\n",
    "sentences = list(df_cleaned_and_filtered['review_body'])\n",
    "for sen in sentences:\n",
    "    X.append(preprocess_text(sen))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101831,)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df_cleaned_and_filtered['is_positive_sentiment']\n",
    "y.head(10)\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer(num_words=5000)\n",
    "tokenizer.fit_on_texts(X_train)\n",
    "\n",
    "X_train = tokenizer.texts_to_sequences(X_train)\n",
    "X_test = tokenizer.texts_to_sequences(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding 1 because of reserved 0 index\n",
    "vocab_size = len(tokenizer.word_index) + 1\n",
    "\n",
    "maxlen = 100\n",
    "\n",
    "X_train = pad_sequences(X_train, padding='post', maxlen=maxlen)\n",
    "X_test = pad_sequences(X_test, padding='post', maxlen=maxlen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-03-31 06:49:29--  http://nlp.stanford.edu/data/glove.6B.zip\n",
      "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
      "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://nlp.stanford.edu/data/glove.6B.zip [following]\n",
      "--2020-03-31 06:49:30--  https://nlp.stanford.edu/data/glove.6B.zip\n",
      "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: http://downloads.cs.stanford.edu/nlp/data/glove.6B.zip [following]\n",
      "--2020-03-31 06:49:30--  http://downloads.cs.stanford.edu/nlp/data/glove.6B.zip\n",
      "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n",
      "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 862182613 (822M) [application/zip]\n",
      "Saving to: ‘glove.6B.zip’\n",
      "\n",
      "glove.6B.zip        100%[===================>] 822.24M  2.13MB/s    in 6m 28s  \n",
      "\n",
      "2020-03-31 06:55:58 (2.12 MB/s) - ‘glove.6B.zip’ saved [862182613/862182613]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget http://nlp.stanford.edu/data/glove.6B.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  glove.6B.zip\n",
      "  inflating: glove.6B.50d.txt        \n",
      "  inflating: glove.6B.100d.txt       \n",
      "  inflating: glove.6B.200d.txt       \n",
      "  inflating: glove.6B.300d.txt       \n"
     ]
    }
   ],
   "source": [
    "!unzip glove.6B.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import array\n",
    "from numpy import asarray\n",
    "from numpy import zeros\n",
    "\n",
    "embeddings_dictionary = dict()\n",
    "glove_file = open('./glove.6B.100d.txt', encoding=\"utf8\")\n",
    "\n",
    "for line in glove_file:\n",
    "    records = line.split()\n",
    "    word = records[0]\n",
    "    vector_dimensions = asarray(records[1:], dtype='float32')\n",
    "    embeddings_dictionary [word] = vector_dimensions\n",
    "glove_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_matrix = zeros((vocab_size, 100))\n",
    "for word, index in tokenizer.word_index.items():\n",
    "    embedding_vector = embeddings_dictionary.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        embedding_matrix[index] = embedding_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install bert-for-tf2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n"
     ]
    }
   ],
   "source": [
    "#import tensorflow_hub as hub\n",
    "\n",
    "model = Sequential()\n",
    "embedding_layer = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=maxlen , trainable=False)\n",
    "#embedding_layer = hub.KerasLayer(\"https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/1\",\n",
    "#                            trainable=False)\n",
    "model.add(embedding_layer)\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1, activation='sigmoid'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 100, 100)          3983000   \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 10000)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 10001     \n",
      "=================================================================\n",
      "Total params: 3,993,001\n",
      "Trainable params: 10,001\n",
      "Non-trainable params: 3,983,000\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
    "\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Train on 65171 samples, validate on 16293 samples\n",
      "Epoch 1/6\n",
      "65171/65171 [==============================] - 2s 34us/step - loss: 0.5218 - acc: 0.7423 - val_loss: 0.4806 - val_acc: 0.7700\n",
      "Epoch 2/6\n",
      "65171/65171 [==============================] - 2s 29us/step - loss: 0.4436 - acc: 0.7921 - val_loss: 0.4788 - val_acc: 0.7629\n",
      "Epoch 3/6\n",
      "65171/65171 [==============================] - 2s 29us/step - loss: 0.4201 - acc: 0.8088 - val_loss: 0.4598 - val_acc: 0.7859\n",
      "Epoch 4/6\n",
      "65171/65171 [==============================] - 2s 29us/step - loss: 0.4072 - acc: 0.8176 - val_loss: 0.4629 - val_acc: 0.7876\n",
      "Epoch 5/6\n",
      "65171/65171 [==============================] - 2s 27us/step - loss: 0.3977 - acc: 0.8227 - val_loss: 0.4983 - val_acc: 0.7816\n",
      "Epoch 6/6\n",
      "65171/65171 [==============================] - 2s 28us/step - loss: 0.3898 - acc: 0.8267 - val_loss: 0.4662 - val_acc: 0.7897\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, batch_size=128, epochs=6, verbose=1, validation_split=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20367/20367 [==============================] - 1s 41us/step\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, y_test, verbose=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.4746249426111836\n",
      "Test Accuracy: 0.7840133309364319\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Score:\", score[0])\n",
    "print(\"Test Accuracy:\", score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 393
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 393
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "         \n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "only had problems with updating my downloads otherwise was very easy \n",
      "1\n"
     ]
    }
   ],
   "source": [
    "instance = X[19724]\n",
    "print(instance)\n",
    "\n",
    "print(y[19724])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00236707]], dtype=float32)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instance = tokenizer.texts_to_sequences(instance)\n",
    "\n",
    "flat_list = []\n",
    "for sublist in instance:\n",
    "    for item in sublist:\n",
    "        flat_list.append(item)\n",
    "\n",
    "flat_list = [flat_list]\n",
    "\n",
    "instance = pad_sequences(flat_list, padding='post', maxlen=maxlen)\n",
    "\n",
    "model.predict(instance)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.13348608]], dtype=float32)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_review_body = 'This product sucks!'\n",
    "instance = tokenizer.texts_to_sequences(sample_review_body)\n",
    "\n",
    "flat_list = []\n",
    "for sublist in instance:\n",
    "    for item in sublist:\n",
    "        flat_list.append(item)\n",
    "\n",
    "flat_list = [flat_list]\n",
    "\n",
    "instance = pad_sequences(flat_list, padding='post', maxlen=maxlen)\n",
    "\n",
    "model.predict(instance)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00443701]], dtype=float32)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_review_body = 'Highly recommended.  This product is great!!'\n",
    "instance = tokenizer.texts_to_sequences(sample_review_body)\n",
    "\n",
    "flat_list = []\n",
    "for sublist in instance:\n",
    "    for item in sublist:\n",
    "        flat_list.append(item)\n",
    "\n",
    "flat_list = [flat_list]\n",
    "\n",
    "instance = pad_sequences(flat_list, padding='post', maxlen=maxlen)\n",
    "\n",
    "model.predict(instance)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_python3",
   "language": "python",
   "name": "conda_python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
